Publications

E. Cherif, T. Kattenborn, L. A. Brown, M. Ewald, K. Berger, P. D. Dao, T. B. Hank, E. Laliberté, B. Lu, and H. Feilhauer. Uncertainty Assessment in Deep Learning-based Plant Trait Retrievals from Hyperspectral data. EGUsphere, (2025):1--35, 2025. [PUMA: data deep_learning hyperspectral xack yaff] URL

Vincent D Friedrich, Peter Pennitz, Emanuel Wyler, Julia M Adler, Dylan Postmus, Luiz Gustavo Teixeira Alves, Julia Prigann, Fabian Pott, Daria Vladimirova, Thomas Hoefler, Cengiz Goekeri, Markus Landthaler, Christine Goffinet, Antoine-Emmanuel Saliba, Markus Scholz, Martin Witzenrath, Jakob Trimpert, Holger Kirsten, and Geraldine Nouailles. Neural Network-Assisted Humanization of COVID-19 Hamster scRNAseq Data Reveals Matching Severity States in Human Disease. Cold Spring Harbor Laboratory, January 2024. [PUMA: HCOVID-19 Severity States scRNAseq yaff xack data] URL

Beth Russell, Katharina Beyer, Ailbhe Lawlor, Monique J. Roobol, Lionne D.F. Venderbos, Sebastiaan Remmers, Erik Briers, Sara J. MacLennan, Steven MacLennan, Muhammad Imran Omar, Mieke Van Hemelrijck, Emma Smith, James N’Dow, Karin Plass, Maria Ribal, Nicolas Mottet, Robert Shepherd, Tom Abbott, Ken Mastris, Lisa Moris, Michael Lardas, Thomas Van den Broeck, Peter-Paul Willemse, Nicola Fossati, Karl Pang, Riccardo Campi, Isabella Greco, Mauro Gacci, Sergio Serni, Anders Bjartell, Ragnar Lonnerbro, Alberto Briganti, Daniele Crosti, Roberto Garzonio, Giorgio Gandaglia, Martina Faticoni, Grant office, Chris Bangma, Maria Jongerden, Derya Tilki, Anssi Auvinen, Teemu Murtola, Tapio Visakorpi, Kirsi Talala, Teuvo Tammela, Aino Siltari, Stephane Lejeune, Laurence Colette, Simona Caputova, Delielena Poli, Sophie Byrne, Luz Fialho, Ashley Rowland, Neo Tapela, Nicola Di Flora, Kathi Apostolidis, Valerie Lemair, Bertrand De Meulder, Charles Auffray, Nesrine Taibi, Ayman Hijazy, Albert Saporta, Kai Sun, Shaun Power, Nazanin Zounemat Kermani, Kees van Bochove, Azadeh Tafreshiha, Chiara Bernini, Denis Horgan, Louise Fullwood, Marc Holtorf, Doron Lancet, Gabi Bernstein, Sheela Tripathee, Manfred Wirth, Michael Froehner, Beate Brenner, Angelika Borkowetz, Christian Thomas, Friedemann Horn, Kristin Reiche, Markus Kreuz, Andreas Josefsson, Delila Gasi Tandefelt, Jonas Hugosson, Jack Schalken, Henkjan Huisman, Thomas Hofmarcher, Peter Lindgren, Emelie Andersson, Adam Fridhammar, Monica Tames Grijalva, Susan Evans-Axelsson, Frank Verholen, Jihong Zong, John-Edward Butler-Ransohoff, Todd Williamson, Reg Waldeck, Amanda Bruno, Ekaterina Nevedomskaya, Samuel Fatoba, Niculae Constantinovici, Carl Steinbeisser, Monika Maass, Patrizia Torremante, Emmanuelle Dochy, Federica Pisa, Marc Dietrich Voss, Kishore Papineni, Jing Wang silvanto, Robert Snijder, Xuewei Wang, Mark Lambrecht, Russ Wolfinger, Sherinne Eid, Soundarya Palanisamy, Samiul Haque, Laurent Antoni, Angela Servan, Katie Pascoe, Paul Robinson, Joana Lencart, Bertrand Jaton, Heidi Turunen, Olavi Kilkku, Pasi Pohjanjousi, Olli Voima, Liina Nevalaita, Keijo Punakivi, Sarah Seager, Shilpa Ratwani, Katarzyna Grzeslak, James Brash, Elaine Longden-Chapman, Danny Burke, Muriel Licour, Sarah Payne, Alan Yong, Flavia Lujan, Sophia Le Mare, Jan Hendrich, Michael Bussmann, Juckeland, Kotik, Delielena Poli, and Christian Reich. Survivorship Data in Prostate Cancer: Where Are We and Where Do We Need To Be?. European Urology Open Science, (59):27–29, Elsevier BV, January 2024. [PUMA: Cancer: Prostate Survivorship imported zno data] URL

Kimberly E Roche, Johannes R Bjork, Mauna R Dasari, Laura Grieneisen, David Jansen, Trevor J Gould, Laurence R Gesquiere, Luis B Barreiro, Susan C Alberts, Ran Blekhman, Jack A Gilbert, Jenny Tung, Sayan Mukherjee, and Elizabeth A Archie. Universal gut microbial relationships in the gut microbiome of wild baboons. Elife, (12)eLife Sciences Publications, Ltd, May 2023. [PUMA: bacteria between community correlations cynocephalus data disease dynamics ecology gut infectious longitudinal microbiology microbiome microbiota personalization universality zno] URL

Peter Christen, and Rainer Schnell. Thirty-three myths and misconceptions about population data: from data capture and processing to linkage. Int. J. Popul. Data Sci., (8)1:2115, Swansea University, January 2023. [PUMA: administrative data editing errors linkage personal quality record xack]

Johannes Frey, Marvin Hofer, and Sebastian Hellmann. Studying Linked Data Accessibility Healthiness for the Long Tail of the Data Web.. QuWeDa/MEPDaW@ ISWC, 55--64, 2023. [PUMA: Accessibility Healthiness Linked Long Tail xack data web]

Lucas Lange, Tobias Schreieder, Victor Christen, and Erhard Rahm. Slice it up: Unmasking user identities in smartwatch health data. 2023. [PUMA: data health smartwatch xack]

Parvaneh Joharinad, and Jürgen Jost. Mathematical principles of topological and geometric data analysis. Springer International Publishing, Cham, 2023. [PUMA: Mathematical analysis data geometric nopdf topological]

Oliver Kirsten, Martin Bogdan, and Sophie Adama. Evaluating the DoC-Forest tool for Classifying the State of Consciousness in a Completely Locked-In Syndrome Patient. 2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC), 37-41, 2023. [PUMA: Complexity Computational Consciousness Information Locked-In Measures Modeling Neuroscience Prediction Predictive Processing Signal Syndrome Theory Training algorithms and data learning modeling models processing zno machine]

Maja Schneider. Distributed, Privacy-Aware Location Data Aggregation. 2024. [PUMA: Aggregation Distributed Location Privacy-Aware xack data] URL

Ekaterina Borisova, Raia Abu Ahmad, Georg Rehm, Ricardo Usbeck, Jennifer D’Souza, Markus Stocker, Sören Auer, Judith Gilsbach, Anastasia Wolschewski, Johannes Keller, Daniel Schneider, Thomas Neumuth, and Sonja Schimmler. NFDI4DS Transfer and Application. Gesellschaft für Informatik e.V., 2023. [PUMA: Infrastructures Intelligence NFDI NFDI4DS Research Science zno artificial data] URL

Sonja Schimmler, Bianca Wentzel, Arnim Bleier, Stefan Dietze, Saurav Karmakar, Peter Mutschke, Angelie Kraft, Tilahun A. Taffa, Ricardo Usbeck, Zeyd Boukhers, Sören Auer, Leyla J. Castro, Marcel R. Ackermann, Thomas Neumuth, Daniel Schneider, Ziawasch Abedjan, Atif Latif, Fidan Limani, Raia Abu Ahmad, Georg Rehm, Sima Attar Khorasani, and Matthias Lieber. NFDI4DS Infrastructure and Services. Gesellschaft für Informatik e.V., 2023. [PUMA: Infrastructures NFDI NFDI4DS Research zno data] URL

Aris Marcolongo, Mykhailo Vladymyrov, Sebastian Lienert, Nadav Peleg, Sigve Haug, and Jakob Zscheischler. Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks. Environmental Data Science, (1):e2, 2022. [PUMA: Convolutional Predicting data gross low primary production weather zno networks neural]

Bernhard Vogginger, Amirhossein Rostami, Vaibhav Jain, Sirine Arfa, Andreas Hantsch, David Kappel, Michael Schäfer, Ulrike Faltings, Hector A. Gonzalez, Chen Liu, and Christian Mayr. Neuromorphic hardware for sustainable AI data centers. Zenodo, 2024. [PUMA: AI Neuromorphic area_responsibleai centers data hardware sustainable topic_lifescience xack] URL

Markus Bauer, and Christoph Augenstein. Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.. Proceedings of the 18th Conference on Computer Science and Intelligence Systems, (35):93–101, IEEE, September 2023. [PUMA: Comparative Computer Self-Supervised Study Unlabelled Vision yaff data learning] URL

Weizhou Luo, Zhongyuan Yu, Rufat Rzayev, Marc Satkowski, Stefan Gumhold, Matthew McGinity, and Raimund Dachselt. PEARL: Physical Environment based Augmented Reality Lenses for In-Situ Human Movement Analysis. CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Apr 19, 2023. [PUMA: Analytics, FIS_scads Immersive In-situ affordance, analysis, augmented/mixed data movement physical reality, referents, topic_visualcomputing visualization xack]

Marzan Tasnim Oyshi, Sebastian Vogt, and Stefan Gumhold. TmoTA: Simple, Highly Responsive Tool for Multiple Object Tracking Annotation. In Albrecht Schmidt, Kaisa Väänänen, EditoTesh Goyal, Per Ola Kristensson, Anicia Peters, Stefanie Mueller, Julie R. Williamson, and Max L. Wilson (Eds.), CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, Apr 19, 2023. [PUMA: FIS_scads data labeling labeling, manual sequence topic_visualcomputing video xack] URL

Elena Williams, Manuel Kienast, Evelyn Medawar, Janis Reinelt, Alberto Merola, Sophie Anne Ines Klopfenstein, Anne Rike Flint, Patrick Heeren, Akira-Sebastian Poncette, Felix Balzer, Nico Scherf, and others. A standardized clinical data harmonization pipeline for scalable ai application deployment (fhir-dhp): Validation and usability study. JMIR Medical Informatics, (11):e43847, JMIR Publications Toronto, Canada, 2023. [PUMA: (fhir-dhp) ai application clinical data deployment harmonization pipeline scalable standardized topic_neuroinspired zno]

Johannes Gerritzen, Andreas Hornig, Benjamin Gröger, and Maik Gude. A Data Driven Modelling Approach for the Strain Rate Dependent 3D Shear Deformation and Failure of Thermoplastic Fibre Reinforced Composites: Experimental Characterisation and Deriving Modelling Parameters. Journal of Composites Science, (6)10:318, MDPI, 2022. [PUMA: 3D Approach Characterisation Composites Deformation Dependent Driven Experimental Failure Fibre Modelling Rate Reinforced Shear Strain Thermoplastic topic_engineering zno data]

Patrick Ebel, Pavlo Bazilinskyy, Angel Hsing-Chi Hwang, Wendy Ju, Hauke Sandhaus, Aravinda Ramakrishnan Srinivasan, Qian Yang, and Philipp Wintersberger. Breaking Barriers: Workshop on Open Data Practices in AutoUI Research. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 227--230, 2023. [PUMA: AutoUI Open Practices Research Workshop topic_visualcomputing yaff data]

Patrick Ebel, Kim Julian Gülle, Christoph Lingenfelder, and Andreas Vogelsang. Exploring Millions of User Interactions with ICEBOAT: Big Data Analytics for Automotive User Interfaces. Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 81--92, 2023. [PUMA: Analytics Automotive Big ICEBOAT Interactions Interfaces User topic_visualcomputing zno data]

Patrick Ebel, Ibrahim Emre Göl, Christoph Lingenfelder, and Andreas Vogelsang. Destination Prediction Based on Partial Trajectory Data. 2020. [PUMA: Based Destination Partial Prediction Trajectory on zno data] URL

Patrick Ebel, Kim Julian Gülle, Christoph Lingenfelder, and Andreas Vogelsang. ICEBOAT: An Interactive User Behavior Analysis Tool for Automotive User Interfaces. Adjunct Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, Association for Computing Machinery, New York, NY, USA, 2022. [PUMA: Design Driving Human-Computer In-Vehicle Information Interaction Naturalistic System Tools Visualization topic_visualcomputing zno data] URL

Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini, and Christian L. Müller. Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (cs.LG) (math.NA) (physics.data-an) Analysis Computer FOS Mathematics Numerical Physical Probability Statistics data information learning machine sciences xack] URL

Fabian Gärtner, Christian Höner Zu Siederdissen, Lydia Müller, and Peter F Stadler. Coordinate systems for supergenomes. Algorithms Mol. Biol., (13)1:15, Springer Science and Business Media LLC, September 2018. [PUMA: Betweenness Big Colored Combinatorial Comparative Graph data genomics multigraph optimization ordering theory transcriptomics xack yaff]

Daniel Ayala, Inma Hernández, David Ruiz, and Erhard Rahm. Multi-source dataset of e-commerce products with attributes for property matching. Data Brief, (41)107884:107884, Elsevier BV, April 2022. [PUMA: Ontology Property area_bigdata data engineering integration matching zno]

Katja Hoffmann, Katja Cazemier, Christoph Baldow, Silvio Schuster, Yuri Kheifetz, Sibylle Schirm, Matthias Horn, Thomas Ernst, Constanze Volgmann, Christian Thiede, Andreas Hochhaus, Martin Bornhäuser, Meinolf Suttorp, Markus Scholz, Ingmar Glauche, Markus Loeffler, and Ingo Roeder. Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology. BMC Med. Inform. Decis. Mak., (20)1:28, February 2020. [PUMA: Clinical Computer Haematology Individual Mathematical Model-based Routine Support decision-making management modelling optimization planning simulation system therapy treatment workflow zno data]

David Nam, Julius Chapiro, Valerie Paradis, Tobias Paul Seraphin, and Jakob Nikolas Kather. Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep., (4)4:100443, Elsevier BV, April 2022. [PUMA: AI CNN Communications DICOM Diagnosis Digital HCC Imaging Individual ML MVI Medicine NAFLD NASH Prognosis Reporting TACE TRIPOD Transparent WSIs a and artificial carcinoma chemoembolisation convolutional data deep diagnostic disease fatty for hepatocellular images imaging in integration intelligence invasion learning liver machine microvascular model multimodal multivariable network neural non-alcoholic of or prediction slide steatohepatitis support system topic_lifescience transarterial whole zno]

Pascal Kerschke, Holger H Hoos, Frank Neumann, and Heike Trautmann. Automated algorithm selection: Survey and perspectives. Evol. Comput., (27)1:3--45, MIT Press, 2019. [PUMA: algorithm analysis approaches automated combinatorial configuration continuous data exploratory feature-based landscape learning machine metalearning optimisation selection streams zno]

Diego Esteves, Anisa Rula, Aniketh Janardhan Reddy, and Jens Lehmann. Toward Veracity Assessment in RDF Knowledge Bases: An Exploratory Analysis. J. Data and Information Quality, (9)3Association for Computing Machinery, New York, NY, USA, February 2018. [PUMA: DeFacto, analysis benchmark, checking, data data, exploratory fact linked quality, trustworthiness, zno] URL

Akshay Akshay, Mitali Katoch, Navid Shekarchizadeh, Masoud Abedi, Ankush Sharma, Fiona C Burkhard, Rosalyn M Adam, Katia Monastyrskaya, and Ali Hashemi Gheinani. Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis. Gigascience, (13)January 2024. [PUMA: AutoML analysis classification data learning machine problems topic_federatedlearn visualization xack yaff]